






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer


class 							__UpperCamelCase     (  tf.keras.layers.Layer ):







	def __init__(	self   :				List[Any]							,					_lowerCAmelCase   :				Dict							,					_lowerCAmelCase   :				Dict							,					_lowerCAmelCase   :				Optional[int] = None							,					_lowerCAmelCase   :				List[Any] = None      )		->			Any:
		"""simple docstring"""







		super().__init__()
		__lowercase   							=	pad_token_id
		__lowercase   							=	max_length
		__lowercase   							=	vocab
		__lowercase   							=	merges
		__lowercase   							=	BytePairTokenizer(_lowerCAmelCase							,					_lowerCAmelCase							,					sequence_length=_lowerCAmelCase      )







	@classmethod
	def 			_a (	cls   :				Optional[Any]							,					_lowerCAmelCase   :				str							,					*_lowerCAmelCase   :				Tuple							,					**_lowerCAmelCase   :				Tuple      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	[""" """.join(_lowerCAmelCase      ) for m in tokenizer.bpe_ranks.keys()]
		__lowercase   							=	tokenizer.get_vocab()
		return cls(_lowerCAmelCase							,					_lowerCAmelCase							,					*_lowerCAmelCase							,					**_lowerCAmelCase      )







	@classmethod
	def 			_a (	cls   :				str							,					_lowerCAmelCase   :				Dict							,					*_lowerCAmelCase   :				Dict							,					**_lowerCAmelCase   :				Union[str, Any]      )		->			List[str]:
		"""simple docstring"""







		__lowercase   							=	GPTaTokenizer.from_pretrained(_lowerCAmelCase							,					*_lowerCAmelCase							,					**_lowerCAmelCase      )
		return cls.from_tokenizer(_lowerCAmelCase							,					*_lowerCAmelCase							,					**_lowerCAmelCase      )







	@classmethod
	def 			_a (	cls   :				Union[str, Any]							,					_lowerCAmelCase   :				Union[str, Any]      )		->			Optional[Any]:
		"""simple docstring"""







		return cls(**_lowerCAmelCase      )







	def 			_a (	self   :				str      )		->			Any:
		"""simple docstring"""







		return {
		    "vocab": self.vocab,
		    "merges": self.merges,
		    "max_length": self.max_length,
		    "pad_token_id": self.pad_token_id,
		}







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				Union[str, Any]							,					_lowerCAmelCase   :				str = None      )		->			int:
		"""simple docstring"""







		__lowercase   							=	self.tf_tokenizer(_lowerCAmelCase      )
		__lowercase   							=	tf.ones_like(_lowerCAmelCase      )

		if self.pad_token_id is not None:
			# pad the tokens up to max length
			__lowercase   							=	max_length if max_length is not None else self.max_length

			if max_length is not None:
				__lowercase							,    __lowercase   							=	pad_model_inputs(
				    _lowerCAmelCase							,					max_seq_length=_lowerCAmelCase							,					pad_value=self.pad_token_id      )

		return {"attention_mask": attention_mask, "input_ids": input_ids}







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["DebertaTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "DebertaForMaskedLM",
      "DebertaForQuestionAnswering",
      "DebertaForSequenceClassification",
      "DebertaForTokenClassification",
      "DebertaModel",
      "DebertaPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFDebertaForMaskedLM",
      "TFDebertaForQuestionAnswering",
      "TFDebertaForSequenceClassification",
      "TFDebertaForTokenClassification",
      "TFDebertaModel",
      "TFDebertaPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



